Design and use paradigms for Gazebo, an open-source multi-robot simulator

By Nathan P. Koenig, Andrew Howard. 2004

3,832 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Control and Systems Engineering, Robotics & Control

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Citations in the corpus

Deep Reinforcement Learning for Autonomous Driving: A Survey (2020, methodology): “…positions, testing control policies for vehicles AIRSIM [104] Camera stream with depth and semantic segmentation, support for drones GAZEBO (ROS) [105] Multi-robot physics simulator employed for path planning & vehicle control in complex 2D & 3D maps SUMO [106] Macro-scale modelling of trafﬁc in…”
A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation (2016, positive): “The translated Gazebo images are then fed to the DRL policy network to give control commands.”
Beauty and the Beast: Optimal Methods Meet Learning for Drone Racing (2018, methodology): “We use RotorS [6] and Gazebo [12] for all simulation experiments.”
A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, background): “Gazebo [295] is a common simulation environment for robotics.”
Agile Autonomous Driving using End-to-End Deep Imitation Learning (2017, methodology): “Our proposed framework is based on Differential Dynamic Programming (DDP) [29] 13 , at each iteration we create a local model along a nominal trajectory through the belief space (¯ b k , ¯ u k ) including: 1) a linear approximation of the belief dynamics model; 2) a second-order local approximation…”
Simitate: A Hybrid Imitation Learning Benchmark (2019, positive): “Exemplary, we provide integration into two widely used simulations [48], [49] in the robotics and machine learning community.”
MINOS: Multimodal Indoor Simulator for Navigation in Complex Environments (2017, methodology): “The Gazebo simulator (Koenig and Howard, 2014) has been used extensively in robotics research.”
BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning (2018, background): “Currently, most general-purpose simulation frameworks do not feature language, such as PycoLab (DeepMind, 2017), MazeBase (Sukhbaatar et al., 2015), Gazebo (Koenig and Howard, 2004), Viz-Doom (Kempka et al., 2016), DM-30 (Espeholt et al., 2018), and AI2-Thor (Kolve et al., 2017).”
AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles (2017, background): “Gazebo [13] has been one the most popular simulation platforms for the research work.”
SAPIEN: A SimulAted Part-Based Interactive ENvironment (2020, background): “However, for real-world robotics, the simulation and client response need to be asynchronous [24] and separated.”
Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning (2020, methodology): “To evaluate the performance of the trained models in an unknown environment, a new Gazebo environment was applied, as shown in Fig.”
Multiple‐Robot Simultaneous Localization and Mapping: A Review (2016, methodology): “Robot operating system (ROS) (Willow Garage, 2006) and Gazebo (Koenig and Howard, 2004), Microsoft Robotics Developer Studio (MRDS, 2006), virtual robot experimentation platform (V-REP) (Coppelia Robotics, 2013) are just a few examples of such simulators.”
A direct method for trajectory optimization of rigid bodies through contact (2014, background): “Indeed, the multi-contact dynamics engines used to simulate grasping [21, 23] do not use hybrid models of the dynamics, because the permutations of different possible modes grows exponentially with the number of links and contact points, and because hybrid models can be plagued by infinitely-frequen”
A compilation of UAV applications for precision agriculture (2020, positive): “The functionality of UAV was ﬁrstly simulated utilising the Hector UAV package [89] in Gazebo [90] .”
Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives (2023, methodology): “method into the intelligent vehicle field, they define five different driving maneuvers in the Gazebo simulator [86], and the vehicle chooses a corresponding maneuver based on the image information.”
LGSVL Simulator: A High Fidelity Simulator for Autonomous Driving (2020, background): “Gazebo [16] is one of the most popular simulation platforms used in robotics and related research areas.”
High-fidelity simulation for evaluating robotic vision performance (2016, background): “Popular robot simulators such as Gazebo [9] and Player/Stage [6] have existed for many years, and are commonly used to test robot motion and control.”
Computing Systems for Autonomous Driving: State of the Art and Challenges (2020, methodology): “open-source projects, such as CARLA and Gezabo, for urban driving or robotics simulations [255], [256].”